Internet-based system for evaluating T waves within ECG waveforms to determine the presence of cardiac abnormalities

ABSTRACT

The present invention provides an improved, Internet-based system that seamlessly collects cardiovascular data from a patient before, during, and after a procedure for EP or an ID. During an EP procedure, the system collects information describing the patient&#39;s response to PES and the ablation process, ECG waveforms and their various features, HR and other vital signs, HR variability, cardiac arrhythmias, patient demographics, and patient outcomes. Once these data are collected, the system stores them on an Internet-accessible computer system that can deploy a collection of user-selected and custom-developed algorithms. Before and after the procedure, the system also integrates with body-worn and/or programmers that interrogate implanted devices to collect similar data while the patient is either ambulatory, or in a clinic associated with the hospital. A data-collection/storage module, featuring database interface, stores physiological and procedural information measured from the patient.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/711,084, filed Oct. 8, 2012, which is hereby incorporated in itsentirety including all tables, figures, and claims.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

None.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to systems for processing data frompatients undergoing cardiovascular procedures, e.g. electrophysiology(EP) procedures.

Description of the Related Art

Patients with abnormal cardiac rhythms can be treated with EP, orreceive an implanted device (ID), such as a pacemaker or implantablecardioverter-defibrillator (ICD). These therapies and devices areeffective in restoring the patient's cardiac rhythm to a normal level,and are typically characterized by a collection of data-generatingdevices that are used before, during, and after procedures for EP or theID.

Prior to such a procedure, physicians often prescribeelectrocardiography (ECG) monitors that measure time-dependentwaveforms, from which heart rate (HR) and information related toarrhythmias and other cardiac properties are extracted. These systemscan characterize ambulatory patients over short periods (e.g. 24-48hours) using ‘holier’ monitors, or over longer periods (e.g. 1-3 weeks)using cardiac event monitors. Conventional holter or event monitorstypically include a collection of chest-worn ECG electrodes (typically 3or 5), an ECG circuit that collects analog signals from the ECGelectrodes and coverts these into multi-lead ECG waveforms, and acomputer processing unit that analyzes the ECG waveforms to determinecardiac information. Typically the patient wears the entire system ontheir body. Some modern ECG-monitoring systems include wirelesscapabilities that transmit ECG waveforms and other numerical datathrough a cellular interface to an Internet-based system, where they arefurther analyzed to generate, for example, reports describing thepatient's cardiac rhythm. In less sophisticated systems, theECG-monitoring system is worn by the patient, and then returned to acompany that downloads all relevant information into a computer, whichthen analyzes it to generate the report. The report, for example, may beimported into the patient's electronic medical record (EMR). The EMRavails the report to cardiologists or other clinicians, who then use itto help characterize the patient.

To monitor non-ambulatory, hospitalized patients, conventional vitalsign monitors include ECG monitoring systems that characterize apatient's cardiac response in a similar way to holter or event monitors.Such monitors typically measure multi-lead ECG waveforms that areprocessed by embedded software within the monitor to generate ECGwaveforms and determine HR and a wide range of other cardiac properties.

During a conventional EP procedure, software systems can collectphysiological information from the patient (e.g. vital signs and ECGwaveforms), which is then used to help guide the procedure. These dataare also stored in the patient's EMR, where they can be used for futureanalysis by cardiologists and other clinicians. ECG systems used duringEP procedures typically measure 12 leads of ECG waveforms, which acardiologist then interprets to elucidate, diagnose, and ultimatelytreat the electrical activities of the patient's heart. Additionally,during EP, an invasive catheter records spontaneous activity of theheart, as well as cardiac responses to programmed electrical stimulation(PES). In addition to these diagnostic and prognostic procedures, an EPcardiologist uses therapeutic methods, such as radio frequency ablationof pre-determined portions of the heart, to adjust the patient's cardiacrhythm to a relatively stable state. ECG-monitoring devices used in theEP procedure measure the response of the injured or cardiomyopathicmyocardium to PES on specific pharmacological regimens in order toassess the likelihood that the regimen will successfully preventpotentially fatal sustained ventricular tachycardia (VT) or ventricularfibrillation (VF) in the future. Sometimes a series of drug trials areconducted before and/or after an EP procedure to enable the cardiologistto select a regimen for long-term treatment that best prevents or slowsthe development of VT or VF following PES. Other therapeutic modalitiesemployed in this field include antiarrhythmic drug therapy and IDs. Suchstudies may also be conducted in the presence of a newly deployed ID.

Modern IDs also include electronic circuitry for recording and storingcardiac parameters, such as arrhythmia information, HR, HR variability,and data describing the performance of the implanted device. Typicallythe ID stores this information within a computer memory that can beinterrogated over a short-range wireless interface by a specializeddevice within a cardiologist's office called a ‘programmer’. Both theprogrammer and ID are typically designed and manufactured by the samecompany. Medtronic, the world's largest manufacturer of IDs, also makesa software system called ‘Paceart’ that receives, stores, and displaysscheduling information and data generated by all major manufacturers ofIDs, e.g. Medtronic, Boston Scientific, St. Jude, and Biotronix. Theprogrammer typically includes a computer, display, ECG-measuring system,thermal printer, and a wand that is placed over the implanted device toread information over a short-range, wireless interface. Once read, thecomputer stores information generated by the ID, and at a later time canimport this information into the patient's EMR, where it can be used tofurther diagnose the patient.

Many conventional EMRs are large software systems hosted on computerservers within a hospital or medical clinic. Some EMRs reside in ‘thecloud’, meaning they are hosted on remote, Internet-connected computerservers (located, e.g., in a third-party data center), which then rendera graphical user interface (GUI) to hospital clinicians with aconventional web browser. In most instances, hospital administrators andclinicians use either the EMR or a secondary software system toperformancillary functions related to the EP procedure, such asscheduling, billing, and patient follow-up.

SUMMARY OF THE INVENTION

As described above, a collection of hardware and software systems cancollect and store a patient's cardiovascular information before acardiologist conducts a procedure for EP or an ID, during the actualprocedure, and after the patient leaves the hospital or medical clinic.In theory, data during each of these phases flows into the patient'sEMR. But, in reality, even state-of-the-art EMRs are only able tocollect and store limited amounts of data from these systems, especiallywhen multiple, disparate systems are used to monitor the patient. Andtypically the data are not organized or formatted in a way that allowsprocessing large data sets measured before, during, and after an EPprocedure. Analysis of such data, if it were possible, would facilitatesophisticated inter-site clinical studies with a large number ofpatients. This, in turn, could yield analysis and development of newtherapies, devices, and treatment protocols for cardiovascular patients.

With this in mind, the present invention provides an improved,Internet-based system that seamlessly collects cardiovascular data froma patient before, during, and after a procedure for EP or an ID. Forexample, during an EP procedure, the system collects informationdescribing the patient's response to PES and the ablation process, ECGwaveforms and their various features, HR and other vital signs, HRvariability, cardiac arrhythmias, patient demographics, and patientoutcomes. Once these data are collected, the system stores them on anInternet-accessible computer system that can deploy a collection ofuser-selected and custom-developed algorithms. Before and after theprocedure, the system also integrates with body-worn and/or programmersthat interrogate implanted devices to collect similar data while thepatient is either ambulatory, or in a clinic associated with thehospital. A data-collection/storage module, featuring databaseinterface, stores physiological and procedural information measured fromthe patient. Interfacing with the database is a data-analytics modulethat features a collection of algorithm-based tools run by computer code(e.g. software) that can collectively analyze information measuredduring each of these phases from large sets of patients. Thedata-analytics module also includes an Internet-based GUI that rendersthese data and exports them for future analysis. Patients providing datafor this system may be associated with a single site, or multiple,disparate sites. Analysis of the data, for example, can yield reportsthat characterize the efficacy of a given procedure, or help a clinicianimprove a cardiac EP procedure for a given patient. In this way, thepresent invention can facilitate ‘virtual clinical trials’ whereinsophisticated multi-center studies are quickly and efficientlyperformed, all without the significant financial and time investmentsnormally required for conventional clinical trials.

In general, the data-analytics module can perform a spectrum ofcalculations, ranging from simple statistical analyses (e.g. the numberof EP procedures performed by a clinic, or the amount of financialreimbursement received by the clinic) to complex analysis ofphysiological data (e.g. Boolean searches, subsequent analyses, andimage processing). Such analysis can be performed with pre-determinedreporting tools, or by exporting customized data fields that can beanalyzed off-line using custom algorithms.

Software associated with algorithms deployed by the data-analyticsmodule, for example, can analyze numerical vital signs or waveforms,parameters associated with the EP procedure, parameters associated withthe ID, two and three-dimensional images related to the patient'scardiovascular behavior, demographic information, and billing andfinancial information. These data can be analyzed, for example, toestimate or predict the condition of the patient, determine the efficacyof the EP procedure as applied to the patient, evaluate an ID and itsassociated components (e.g. leads), evaluate financial aspects ofhospital or clinic, and evaluate demographics associated withcardiovascular issues. Alternatively, these algorithms can be used forpurposes more suited to scientific research, e.g. for collectivelyanalyzing components of ECG waveforms corresponding to large groups ofpatients receiving a particular EP procedure to estimate the overallefficacy of the procedure. Components of the ECG waveforms analyzed inthis manner include: i) a QRS complex; ii) a P-wave; iii) a T-wave; iv)a U-wave; v) a PR interval; vi) a QRS interval; vii) a QT interval;viii) a PR segment; and ix) an ST segment. The temporal oramplitude-related features of these components may vary over time, andthus the algorithmic-based tools within the system, or softwareassociated with the algorithm-based tools, can analyze thetime-dependent evolution of each of these components. In particular,algorithmic-based tools that perform numerical fitting, mathematicalmodeling, or pattern recognition may be deployed to determine thecomponents and their temporal and amplitude characteristics for anygiven heartbeat recorded by the system.

In one embodiment, for example, ECG waveforms may be numerically ‘fit’with complex mathematical functions, such as multi-order polynomialfunctions or pre-determined, exemplary ECG waveforms. These functionsmay then be analyzed to determine the specific components, or changes inthese components, within the ECG waveform. In related embodiments, ECGwaveforms may be analyzed with more complex mathematical models thatattempt to associate features of the waveforms with specific bioelectricevents associated with the patient.

Each of the above-mentioned components corresponds to a differentfeature of the patient's cardiac system, and thus analysis of themaccording to the invention may determine or predict different cardiacconditions. These conditions and their associated components include:blockage of arteries feeding the heart (each related to the PRinterval); aberrant ventricular activity or cardiac rhythms with aventricular focus (each related to the QRS interval); prolonged time tocardiac repolarization and the onset of ventricular dysrhythmias (eachrelated to the QT interval); P-mitrale and P-pulmonale (each related tothe P-wave); hyperkalemia, myorcardial injury, myocardial ischemia,myocardial infarction, pericarditis, ventricular enlargement, bundlebranch block, and subarachnoid hemorrhage (each related to the T-wave);and bradycardia, hypokalemia, cardiomyopathy, and enlargement of theleft ventricle (each related to the U-wave). These are only a smallsubset of the cardiac conditions that may be determined or estimatedthrough analysis of the ECG waveform according to the invention.

Algorithmic-based tools, or software associated with these tools, canalso analyze relatively long traces of ECG waveforms (spanning overseconds or minutes) measured before, during, and after the EP procedureto characterize: i) a given patient; ii) the efficacy of the EPprocedure applied to that patient; iii) a given patient's need for an EPprocedure; or iv) the overall efficacy of the EP procedure as applied toa group of patients. For example, analysis of relatively long traces ofECG waveforms in this manner may indicate cardiac conditions such ascardiac bradyarrhythmias, blockage of an artery feeding the heart, acutecoronary syndrome, advanced age (fibrosis), inflammation (caused by,e.g., Lyme disease or Chaga's disease), congenital heart disease,ischaemia, genetic cardiac disorders, supraventricular tachycardia suchas sinus tachycardia, atrial tachycardia, atrial flutter, atrialfibrillation, junctional tachycardia, AV nodal reentry tachycardia andAV reentrant tachycardia, reentrant tachycardia, Wolff-Parkinson-White(WPW) Syndrome, Lown-Ganong-Levine (LGL) Syndrome, and ventriculartachycardia. Likewise, analysis of these cardiac conditions by analyzingthe ECG waveforms may indicate the efficacy of the EP procedure.

In one aspect, the invention features a system for evaluating a patientthat includes: i) a first ECG-measuring system that senses ECGinformation from the patient; ii) a data-acquisition system interfacedto a vital sign-monitoring system that senses vital sign informationfrom the patient during a cardiac EP procedure; and iii) a softwaresystem interfaced to both the ECG-measuring system and thedata-acquisition system. The software system typically connects to theInternet, meaning that it can be hosted on a remote server that residesoutside of the hospital. It typically includes a GUI (e.g. a web page),rendered by a web browser, which a user may view with a computer ormobile device, such as a cellular telephone or tablet computer. Thesoftware system features: i) a first software interface that receivesECG information sensed by the ECG-measuring system; ii) a secondsoftware interface that receives vital sign information from thedata-acquisition system and sensed by the vital-sign monitoring system;iii) a database that stores ECG information sensed from the patientbefore and after the EP procedure, and vital sign information sensedduring the EP procedure by the vital sign monitor; and iv) an algorithmthat evaluates the EP procedure by collectively analyzing ECGinformation sensed from the patient before, during and/or after the EPprocedure.

In preferred embodiments, the algorithm compares a first set ofparameters extracted from ECG information sensed from the patient beforethe EP procedure to a second set of parameters extracted from ECGinformation sensed from the patient after the EP procedure. The firstand second sets of data are collected from either an individual patientor large groups of patients. Using this information, the algorithm canestimate the efficacy of a given EP procedure, and convey this in theform of an Internet-accessible report to a clinician. For example,operating in this capacity, the algorithm can analyze HR information,arrhythmia information, or morphology of the ECG waveform, e.g. an ECGQRS complex or QT interval. It then uses this information to evaluate aspecific procedure.

In preferred embodiments, the database is configured to storeinformation from a collection of patients. Here, the system may deployalgorithms that rely on advanced computational techniques, such as anumerical fitting algorithm, mathematical modeling, image analysis,and/or pattern recognition. The algorithm may calculate, for example,statistics describing the efficacy of an EP procedure performed on eachpatient within the group of patients, and following the calculationgenerate a report describing the statistics. In general, the system canperform a wide range of algorithms and, in response generate multipletypes of clinical reports to improve the efficacy of the EP procedure.

In other embodiments, the ECG-measuring system is a body-worn systemthat can include, e.g., an analog ECG front end, a processing system,and an interface to the Internet. The interface can be either wired orwireless, and may include a conventional mobile device, such as acellular telephone or tablet computer. The mobile device used totransmit information to the system may be the same one used to viewreports and GUIs generated by the system. The system can include bothfirst and second ECG-measuring systems than can be the same system, ordifferent systems. Typically the first ECG-measuring system senses ECGinformation from the patient before the EP procedure, and the secondECG-measuring system senses ECG information from the patient after theEP procedure. Both the first and second ECG-measuring systems can bebody-worn systems that are worn on the outside of the patient's body.Alternatively, one or both of the ECG-measuring systems can be animplanted system, e.g. one that comprises a pacemaker or other ID.

In another aspect, the invention provides a system for evaluating adegree of blockage of a patient's heart. The system features a databasethat stores a set of data fields collected from a plurality of patients,with each data field in the set corresponding to an individual patient,and including an ECG waveform and a parameter indicating a degree ofblockage of an artery feeding the individual patient's heart. AnECG-analysis algorithm included in the system processes the ECG waveformin each data field to extract a set of parameters, and correlates theset of parameters to the parameter indicating the degree of blockage. Anaveraging algorithm included in the system processes multiple datafields, with each data field corresponding to an individual patient, todetermine an average correlation factor mapping the set of parameters tothe parameter indicating the degree of blockage. The system alsoincludes an ECG-measuring system (e.g. a body-worn ECG monitor)configured to measure an ECG waveform from a given patient and thentransmit a numerical representation of the ECG waveform to the database(e.g. through a wired or wireless connection). Finally, a correlationalgorithm evaluates the degree of blockage by extracting a new set ofparameters from the ECG waveform measured by the ECG-measuring system,and then comparing the new set of parameters to the average correlationfactor to estimate a new parameter indicating the degree of blockage ofan artery feeding the given patient's heart.

In specific embodiments, the average correlation factor correlates anaverage value of the PR interval to the degree of blockage of an arteryfeeding the heart. For example, when the PR interval exceeds apre-determined value (e.g. about 200 ms), or alternatively when the PRinterval has a temporal variation that exceeds a pre-determined value(e.g. about 25%), the system indicates that a blockage exists for thegiven patient.

In another aspect, the invention provides a system for evaluating apresence of p-mitrale and p-pulmonale in a patient's heart. Here, thesystem includes a database that stores a set of data fields collectedfrom a plurality of patients, with each data field in the setcorresponding to an individual patient and including a value of anamplitude or width of a P wave extracted from an ECG waveform, and aparameter indicating the presence of p-mitrale and p-pulmonale in theindividual patient's heart. An averaging algorithm processes multipledata fields in the database, with each data field corresponding to anindividual patient, to determine an average correlation factor mappingthe value of the amplitude or width of the P wave to the parameterindicating the presence of p-mitrale and p-pulmonale in a patient'sheart. As in the previous aspect, an ECG-measuring system (e.g. abody-worn system) measures an ECG waveform from a given patient and thentransmits a numerical representation of the ECG waveform to thedatabase. Finally, a correlation algorithm evaluates the presence ofp-mitrale and p-pulmonale in the given patient's heart by extracting anew amplitude or width of the P wave from the ECG waveform measured bythe ECG-measuring system, and then comparing the new amplitude of the Pwave to the average correlation factor to estimate a new parameterindicating the presence of p-mitrale and p-pulmonale in the givenpatient's heart. For example, the system may indicate that the patienthas a cardiac condition such as p-mitrale or p-pulmonale when the widthof the P wave is greater than about 50 ms, or the amplitude of the Pwave is greater than about 10% of the amplitude of a QRS complexmeasured from the same ECG waveform.

In another aspect, the invention provides a system for evaluating apresence of at least one of myocardial ischemia, myocardial infarction,pericarditis, ventricular enlargement, bundle branch block, andsubarachnoid hemorrhage. The system uses similar components to thosedescribed above, only it features a database comprising a set of datafields collected from a plurality of patients, with each data field inthe set corresponding to an individual patient and including a T waveextracted from an ECG waveform and a parameter indicating at least oneof the above-described maladies. For example, an inverted T wave mayindicate such a condition.

In yet another aspect, the invention provides a system for evaluating anefficacy of an electrophysiology procedure. The system includes asoftware interface configured to receive ablation locations indicatingwhere a patient's heart has been ablated during a cardiacelectrophysiology (EP) procedure, and store numerical representations ofthe ablation locations in a database. An ECG-measuring system thenmeasures a time-dependent ECG waveform from the patient after the EPprocedure, and transmits the numerical representation of the ECGwaveform to the same database. A first algorithm processes the numericalrepresentations of the time-dependent ECG waveform to determine a heartrate and heart rate variability. And a second algorithm compares a firstset of numerical values indicating the ablation locations to the heartrate and heart rate variability to determine a parameter estimating theefficacy of the EP procedure.

The invention has many advantages. In general, a cloud-based system thatconnects to the Internet from a remote server typically offers moreflexibility than a system that is deployed in the same facility (e.g. ahospital or medical clinic) used to perform the EP procedure. With sucha system, information from multiple, diverse patient groups can becollectively analyzed to perform sophisticated research relating to EPand other cardiovascular procedures. This facilitates ‘virtual clinicaltrials’, as described above, which can be conducted efficiently andinexpensively. The same system that performs the research can alsogenerate reports and other materials using data from large groups ofpatients that can easily be dispersed to clinicians, thereby giving themthe tools to improve their clinical practice. Moreover, Internet-basedsystems, i.e. systems that leverage ‘the cloud’, are inherently easierto maintain (e.g. deploy, update) compared to hosted client-serversystems deployed at a collection of facilities, as new software buildsand enhancements can be made on a single server, and theninstantaneously deployed to multiple Internet-connected sites.

These and other advantages will be apparent from the following detaileddescription, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic drawing of a system according to the inventionthat includes a data-collection/storage module that collectscardiovascular information from a patient and a collection of hardwaredevices and stores the information in a database, and a data-analyticsmodule that collectively analyzes the information to characterize thepatient;

FIG. 2 shows a schematic drawing of the data-collection/storage moduleof FIG. 1;

FIG. 2-1 shows exemplary database tables that describe patientdemographics and physiological information;

FIG. 2-2 depicts schematically the mapping of ECG waveforms collectedfrom a patient to a corresponding data table;

FIG. 3 shows a schematic drawing of the data-analytics module of FIG. 1,featuring an algorithm integrated with the data-collection/storagemodule of FIG. 2 that analyzes a patient's cardiovascular information;

FIG. 4 shows a screenshot from a website associated with thedata-analytics module of FIG. 3 that processes and renders numericaldata stored in the data-collection/storage module of FIG. 2;

FIG. 5 shows a screenshot from a website of FIG. 4 associated with thedata-analytics module that renders a report summarizing a patient'svisit to a medical clinic;

FIG. 6 shows a screenshot from a website of FIG. 4 associated with thedata-analytics module that renders a report summarizing an advisoryaction associated with a ID lead;

FIG. 7 shows a screenshot from a website of FIG. 4 associated with thedata-analytics module that renders a collection of reports summarizing apatient's ID and cardiac response;

FIG. 8 shows a screenshot from a website associated with thedata-analytics module that analyzes ECG waveform data according to astandard analysis;

FIG. 9 shows a screenshot from a website of FIG. 4 associated with thedata-analytics module that analyzes ECG waveform data according to acustom analysis;

FIG. 10 shows a screenshot from a website of FIG. 4 associated with thedata-analytics module that analyzes and plots data from Medtronic'sPaceart system;

FIG. 11 shows a screenshot from a website associated with thedata-analytics module that collects a variety of data fields and exportsthem to a data table for follow-on analysis;

FIG. 12 shows a time-dependent ECG waveform that can be stored in thedata-collection/storage module of FIG. 2 and then analyzed with abeat-picking algorithm;

FIG. 13 shows a multi-lead ECG waveform stored in thedata-collection/storage module of FIG. 2;

FIG. 14 shows a waveform ‘snippet’ taken from an ECG waveform in FIG. 13that indicates various features associated with a patient's cardiaccycle;

FIG. 15 shows a body-worn ECG monitoring system that measures ECGinformation and wirelessly transmits it to an Internet-based systemassociated with the system of FIG. 1; and

FIG. 16 shows a schematic drawing of a ‘Perminova Box’ that collectsdata from device programmers and EP ablations systems, and then forwardsthese data to a data-collection/storage module, and from there to the adata-analytics module.

DETAILED DESCRIPTION OF THE INVENTION

The invention provides an Internet-based system that featuresdata-collection/storage and data-analytics modules that, collectively,allow effective analysis of large data sets to improve cardiovascularmedicine. During use, the data-collection/storage module collectscardiovascular and other patient and device-related data before, during,and after an EP or ID procedure, and then stores data from large groupsof patients in a relational database. It interfaces with adata-analytics module that accesses and processes data from the databasewith a collection of algorithms to yield distilled information, which awebsite then renders for clinicians and other users. In general, thedata collection/storage module integrates with the data-analytics moduleto process data and further distill it to provide useful information toa user. These systems operate in concert to perform a range of numericalanalyses, from simple statistical analyses to complex, multi-parameternumerical studies that investigate patient physiology and efficacies ofspecific procedures.

In embodiments, the system collects information describing ECG waveformsand various components found therein, HR, HR variability, cardiacarrhythmias, performance of IDs, patient demographics, and patientoutcomes, and stores them on an Internet-accessible computer system thatcan deploy a collection of algorithms. Using a simple GUI availablethrough the Internet, clinicians can deploy the algorithms to improvetheir practice and better manage their patients.

FIG. 1 shows a system 100 according to the invention. The system 100 isan Internet-based software system that features a collection of computercode, and typically operates on a remote computer system (e.g. oneresiding in a data center). The system 100 features adata-collection/storage module 101, and a data-analytics module 102. Thedata-collection/storage module 101 features a database 110 that includesan array of data fields and tables that store patient demographics 107,pre-procedure data 106, in-procedure data 104, and post-procedure data103. These data are typically collected by multiple physiologicalmonitoring systems. For example, a patient can wear a telemedicinesystem 166, such as a body-worn ECG monitoring system, to collect thepre-procedure data 106 outside of the hospital. Such a system, which isdescribed in more detail with reference to FIG. 15, typically collectsreal-time ECG waveforms, arrhythmia data, HR data, and other informationfrom the patient during a short period (e.g. a few days to severalweeks) before the actual EP procedure. These data can be ported into thepre-procedure data field 106 through a wired or, more preferably, awireless interface, such as a cellular interface. In embodiments, thetelemedicine system may be programmed to automatically send informationto the data-collection/storage module 101, i.e. it is programmed toautomatically send information to a specific IP address associated withthis system. Alternatively, a third-party vendor may manufacture thetelemedicine system 166, and this in turn integrates with thedata-collection/storage module through a software interface, such as aweb service interface. Here, the web service interface typically sendsan XML file, which is then parsed with a software system residing withthe data-collection/storage module. In other embodiments, thedata-collection/storage module 101 collects pre-procedure physiologicaldata from a collection of telemedicine systems 166, each manufactured byone or more outside vendors. In each case, a schema associated with thepre-procedure data fields 106 is used to describe the specific dataelements that flow into the database 110.

The data-collection/storage module 101 collects data before, during, andafter the actual EP procedure by integrating with a telemedicine system166, EP ablation system 165, data-acquisition system for a vital sign(VS) monitor 164, a programmer 162 that interrogates patients with IDs,and a second telemedicine system 160 for patients without IDs. Each ofthese systems provide numeric and waveform data for thedata-collection/storage module. More specifically, the telemedicinesystems 160, 166 are typically body-worn ECG holter or event monitors,like those described above, that generate ECG waveforms, HR values, andother information describing the patient's cardiac rhythm (e.g. HRvariability, arrhythmia information). The EP ablation systems generatedata describing the EP process, e.g. ablation energies and durations,mapping of where the ablations took place, catheter location, andtime-dependent waveforms generated by catheter-based electrodes thatcome in direct contact with the patient's heart during an EP procedure.The data-acquisition system 164, for example, can include both hardwareand software components that extract numerical and waveform informationfrom a VS monitor, e.g. a serial or parallel data cable and a softwaresystem that receives the data (typically in the form of packets), andthen parses them appropriately. Alternatively, the data-acquisitionsystem 164 can be a software interface to a middleware system (e.g. oneassociated with an EMR) that collects data from the VS monitor. In bothcases, the data-acquisition system 164 extracts time-dependent waveformsand numerical vital signs such as HR, blood pressure, respiratory rate,blood oxygen, and temperature from the VS monitor used during the EPprocedure. In a preferred embodiment, the data-acquisition system 164extracts data in a quasi-continuous manner during the EP procedure, e.g.a new, updated numerical value is extracted every second or so.

The programmer 162 is typically a computer-based system that resides ina cardiologist's office and includes a short-range wireless componentthat, when waved over a patient's ID, receives data and then stores itin memory associated with the computer-based system. For example, theshort-range wireless component may rely on inductive magnetic couplingto remove physiological data associated with the patient, along withdata associated with the performance of the ID, e.g. the number andtime/date of defibrillation shocks, and/or battery information.Programmers also typically include ECG systems designed to measureconventional ECG waveforms and associated information, such as HR. Oncethese and other data are stored on the computer-based system, theprogrammer 162 transfers it over to the post-procedure data field 103for future analysis. In embodiments, for example, the programmer 162 mayinclude manufacturer-specific software, such as Medtronic's PaceartSystem, to facilitate data extraction and transfer. The Paceart Systemorganizes and archives data for cardiac devices across manufacturers andserves as a central repository for a patient's arrhythmia and otherinformation. The system serves as a gateway through which data flowsfrom the computer-based system into a clinic's electronic health recordEMR.

A telemedicine system 160 supplies data for the post-procedure datafield 103 for patients that lack an ID. Such a telemedicine system 160is comparable or, more preferably, identical to the telemedicine system166 used to supply data for the pre-procedure data field 106. It istypically a body-worn system, used to characterize a remote, ambulatorypatient, that includes an ECG-monitoring system and computing modulethat measures, digitizes, and processes analog ECG waveforms todetermine parameters such as HR, arrhythmia information, andmotion-related information from the patient. The telemedicine system 166may include a wireless system that sends data from the ambulatorypatient to the post-procedure data field 103. Typically the telemedicinesystem 160 is worn for a period ranging from 1-2 days to several weeks.In other embodiments, the patient uses the telemedicine system 160 on asemi-permanent basis to collect data for a short period of time eachday. For example, the telemedicine system 160 may be used by thepatient's bedside to collect data each night when the patient issleeping.

Once extracted, data measured from each of these systems 160, 162, 164,165, 166 are stored in the appropriate data fields 103, 104, 106associated with the database 110, and then used for follow-on analysisas described in more detail below. Additionally, the database 110includes data fields that store parameters related to patientdemographics 107, e.g. a patient's name, address, date of birth, age,height, weight, ethnicity, allergies, medications, and social securitynumber.

The data-analytics module 102 features a collection of algorithm-basedtools that interface with the data-collection/storage module 101 toprocess data stored in the pre-procedure 106, in-procedure 104, andpost-procedure 103 data fields to generate usable information for theclinician. In preferred embodiments, the algorithm-based tools provideclinicians with a single, integrated system that allows them to analyzedata collected from a large number of patients associated with differentmedical centers, and in doing so research new treatment strategies thatmay be effective with new patients. For example, the algorithm-basedtools may include modules that facilitate patient follow-up 176, helpdetermine patient outcomes 174, and perform applied 172 and academic 170research studies on large groups of patients to help determine, e.g.,the efficacy of certain treatment methodologies. In embodiments, resultsfrom the applied 172 and academic 170 research studies could be madeavailable to clinicians through reports generated by the system 100.

FIG. 2 shows examples of simple data fields within the database 110associated with the data collection/storage module 101. In embodiments,for example, the database 110 includes a high-level, custom schema 109that describes relationships between data, patients, clinicians, andhospitals. For example, in embodiments the custom schema 109 groupscertain hospitals together which have agreed to share data collectedfrom their respective patients, and also groups clinicians within thehospitals who have privileges to view the data. For research purposes,it will likely be necessary to de-identify these data, e.g. removepersonal patient information as per the guidelines set out by the HealthInsurance Portability and Accountability Act (HIPAA). De-identificationwill remove sensitive personal information, but will retain demographicsinformation that is stored in a patient demographics data field 108featuring simple parameters such as a patient identifier (e.g. number),their gender, date of birth, along with simple biometric parameters suchas weight, height, and whether or not the patient has an ID. Forexample, these data can be organized in standard tables used bycommercially available relational databases, such as PostgreSQL,Microsoft SQL Server, MySQL, IBM DB2, and Oracle. Typically the patientidentifier within the patient demographics field 108 is a database ‘key’that links a particular patient to other data fields. For example, otherdata fields within the database 110, such as the pre-procedure 106,in-procedure 104, and post-procedure 103 data fields, use this key tolink physiological data measured during these particular periods to thepatient. These data are found in new tables 118 a-c in the database, andtypically include physiological data (e.g. numerical values andwaveforms) describing parameters such as HR, systolic and diastolicblood pressure (BP), respiratory rate (RR), and blood oxygen (SpO2).Typically these parameters are measured over time (e.g. in a continuousor quasi-continuous manner), and then identified in the tables 118 a-cby a ‘Run’ number that sequentially increases over time. As describedabove, data for the tables 118 a-c is typically measured with a hardwarecomponent attached to the patient, such as a telemetry monitor that anambulatory patient wears outside of the hospital, an ID, or by a VSmonitor used to measure the patient during an actual EP procedure.

The database may also associate numerical physiological data for eachrun with a physiological waveform 120 a-c that is analyzed to extractthe particular datum. For example, as shown in FIG. 2, theabove-mentioned hardware component may measure time-dependent ECGwaveforms 120 a-c that yield information such as HR and arrhythmiainformation, and are thus stored in the database. Such waveforms may beprocessed with the algorithm-based tools described with reference toFIG. 1, such as numerical ‘fitting’ or beatpicking algorithms, to betterdiagnose a patient's condition. Although FIG. 2 only shows single-leadECG waveforms, other physiological waveforms can also be measured,stored, and then processed with the algorithm-based tools describedabove. These waveforms include multi-lead ECG waveforms,photoplethysmogram (PPG) waveforms that yield SpO2, arterial waveformsthat yield BP, and impedance cardiography (ICG) waveforms that yield RRand cardiac parameters such as stroke volume and cardiac output. Inembodiments, these waveforms may be associated with another table thatincludes annotation markers that indicate fiducial points (e.g., the QRScomplex in an ECG waveform) associated with certain features in thewaveforms. The algorithm-based tools may also process these annotationmarkers to perform simple patient follow-up, estimate patient outcomes,and do applied and academic research, as described above.

In related embodiments, ECG waveforms may be analyzed with more complexmathematical models that attempt to associate features of the waveformswith specific bioelectric events associated with the patient. Forexample, mathematical models can be deployed that estimate ECG waveformsby interactively changing the estimated timing associated withdepolarization and repolarization of a simulated ventricular surface, aswell as the strength of the depolarization and repolarization. Thetimings and signal strengths associated with these models can then becollectively analyzed to simulate an ECG waveform. The simulated ECGwaveform can then be compared to the waveform actually measured from thepatient to help characterize their cardiac condition, or the efficacy ofthe EP procedure that addresses this condition. In general, a wide rangeof physiological and device-related parameters can be stored in the datatables described above. Examples of some of these data fieldscorresponding to specific ECP procedures are shown below in Table 1.

In embodiments, commercially available software tools, such as Mortara'sE-Scribe Rx and VERITASO ECG algorithms, may be interfaced with thedatabase 110 and used to analyze ECG waveforms measured from thepatient. These software tools are designed to analyze complex,multi-lead ECG waveforms to determine complex arrhythmias, VF, VT, etc.

FIG. 3 shows a simple example of a simple data-analytics module 102featuring an algorithm-based tool that analyzes patient data from thedata-collection/storage module to estimate a patient's outcome. In onespecific algorithm associated with the data-analytics module 102,computer code analyzes data fields to first identify patients with IDs(step 130). The code then collects pre-ID (step 132) and in-procedure(step 134) numerical/waveforms data, along with parameters from thepatient's EP procedure (step 136), and readies them for analysis.Parameters collected during the patient's EP procedure includeparameters associated with the EP catheter used during the EP procedure(such as those described in Table 1), potentials applied by the catheterand their timing, and two and three-dimensional images measured duringthe procedure. The algorithm then collectively analyzes these data, andimplements a beat-picking algorithm (step 138) to further characterizeECG waveforms measured during steps 134 and 136. The beat-pickingalgorithm can determine parameters such as induced arrhythmia, effectiverefractory periods, characteristics of specific components within thepatient's ECG waveform, e.g. the QRS complex, width of the P-wave, QTperiod and dispersion, and instantaneous HR.

Using these technologies, the algorithm can perform simple functionslike identifying pre-procedure (step 140) and post-procedure (step 142)arrhythmia occurrences, and then comparing these to determine theefficacy of the procedure (step 144). Many other algorithm-based tools,of course, are possible within the scope of this invention.

Other algorithm-based tools are more sophisticated than that describedwith reference to FIG. 3. In general, these tools can analyze anycombination of data that are generated by the telemedicine systems 160,166, EP ablation system 165, data-acquisition system for the VS monitor164, and programmer 162 described in reference to FIGS. 1 and 2. Datafrom the telemedicine systems 160, 166, VS monitors 164, and programmer162 are described above. Data from the EP ablation system is typicallymore extensive, and is described below in Table 1.

TABLE 1 data fields associated with specific EP procedures Descriptionof # of Possible Data Field Values Example Values Ablated 35 AV NodeModification (Fast pathway), Bundle Branch, Locations Complexfracionated atrial electrograms (CFAE), Crista Terminalis, LAAnteroseptal line, LA CS Line, Left atrium, RIGHT ATRIUM, AccessoryPathway, AV Node, Cavo- tricuspid isthmus, Endocardial, Epicardial, Fastpathway, Intermediate pathway, LEFT CIRCUMFERENTIAL PULMONARY, Segmentalantral left lower pulmonary vein, Segmental antral left lower pulmonaryvein, MITRAL ISTHMUS, Fast pathway, Left Atrial Linear (Mitral Isthmus),Right Circumferential Pulmonary, Left Atrial Linear (Mitral Isthmus),Endocardial, Right Circumferential Pulmonary, EPICARDIAL, Segmentalantral right lower pulmonary vein, Left Atrial Linear (Roof), Segmentalantral right upper pulmonary vein, Segmental antral right upperpulmonary vein, SVC, Slow pathway, Segmental antral right lowerpulmonary vein. Sub-Locations 106 Left Circle, LV Septal Basal, CSmiddle, Lower crista, LA septal wall, Mitral Valve Annulus, RA lateralwall, Left Antero- Lateral, Non-Coronary Cusp, Upper crista, RVOTAnterior, LA Scar, Atrio-Ventricular, Left Lateral, Right Mahaim, CSproximal, Atrio-Fasicular, Right Mid-Septal, RV Posterior Basal, CSdistal, LA appendage, LA anterior wall, Lower Loop, Left Aortic Cusp, LVanterior Fascicle, LA septum, RV Anterior Apical, LV Posterior Mid, LVPosterior Fascicle, LV Posterior Apical, RV Anterior Mid, LLPV, RLPV,RVOT Free Wall, RV Septal Apical, RV Lateral Mid, Mitral Isthmus (withCS), Right Postero-Lateral, RBB, LV Lateral Basal, Left Antero-Septal,RA septal wall, LV Septal Apical, MVA anterior, LV Outlow Tract, UpperLoop, Pulmonary Artery, Right Antero-Lateral, TVA lateral, Right AorticCusp, RA Scar, Right Posterior, RA anterior wall, Mitral Isthmus(endocardial only), RV Posterior Apical, CSos, LV Anterior Mid, RVLateral Basal, Left Mahaim, TVA posterior, RA poseterior wall,Nodo-Fasicular, LV Lateral Mid, RA appendage, Cavo- Tricuspid Isthmus,LA lateral wall, RVOT Posterior, Middle crista, Superior Vena Cava, LeftPosterior, LV Anterior Basal, Fossa ovalls, LV Septal Mid, LUPV,Diverticular, Diverticuar, SVC, Non-Coronary Aortic Cusp, TVA anterior,Right Lateral, RVOT Septal, MVA septal, RUPV, LA posterior wall, RightPostero-Septal, MVA posterior, Nodo-Ventricular, MVA lateral, RVAnterior Basal, LV Lateral Apical, Left Postero- Septal, RightAntero-Septal, LVOT, RV Septal Mid, Left Postero-Lateral, RV SeptalBasal, LA roof, Left bundle branch, LA poseterior wall, RV PosteriorMid, RA septum, RV Outflow Tract Anterior, RV Lateral Apical, Csos, LVPosterior Basal, Right Circle Access 29 Left Subclavian Vein, RightAntecubital Vein, Right Femoral Locations Vein, Right Subclavian Vein,Right Lower Extremeties/Thigh, Left Antecubital Vein, Superficial RightLeg, Superficial Right Hand/Forearm Vein, Deep Right Hand/Forearm Vein,Right Femoral Artery, Superficial Right Arm Vein, Superficial LeftHand/Forearm Vein, Deep Right Arm Vein, Deep Right Arm Vein, Deep LeftHand/Forearm Vein, Left Femoral Vein, Left Lower Extremeties/Thigh,Right Foot, Right Internal Jugular Vein, Superficial Left Leg, DeepRight Leg, Left Femoral Artery, Left Internal Jugular Vein, Deep LeftArm Vein, Left Radial Artery, Right Radial Rrtery, Superficial Left ArmVein, Left Foot, Deep Left Leg Arrhythmia 20 Idiopathic ventriculartachycardia, Atrial Fibrillation Mechanism Paroxysmal, AV Nodal Reentry(fast-slow), AV Nodal Reentry (slow-slow), Premature ventricularcontractions, Atrial Fibrillation Persistent, Atypical Left AtrialFlutter, Atypical Mitral Isthmus Flutter, Bundle Branch Reentry VT,Inappropriate Sinus Tachycardia, Structural ventricular tachycardia -Dilated Cardi, AV Nodal Reentry (slow-fast), Focal Atrial Tachycardia,Antidromic AV reentrant tachycardia, Reverse Typical Atrial Flutter,Atypical Right Atrial Flutter, Typical Atrial Flutter, Structuralventricular tachycardia - Ischemic Card, Wolff-Parkinson-White syndrome,Orthodromic AV reentrant tachycardia Arrhythmia 10 Typical AtrialFlutter, AV nodal reentry (slow-slow), AV nodal Mechanism reentry(slow-fast), Antidromic AV reentrant tachycardia Types (ART), ReverseTypcial Atrial Flutter, Ventricular tachycardia, Orthodromic AVreentrant tachycardia (ORT), Atrial Fibrillation, Atypical AtrialFlutter, AV nodal reentry (fast- slow) Arrhythmia 9 Vagal Effect,Arrhythmogenic Veins RUPV, Arrhythmogenic Observations Veins LLPV,Concealed Accessory Pathway, Negative CSM, WPW, Positive CSM,Arrhythmogenic Veins LUPV, Arrhythmogenic Veins RLPV Axis Deviations 6Left, Left Inferior, None, Right Inferior, Right, Left Superior Mapping8 Carto 3D electro-anatomical, Fluoroscopy, Ensite 3D Balloon SystemsArray, ESI NavX 3D electro-anatomical Energy Sources 6 Cryoablation,Laser, Ultrasound, Other, Radiofrequency Morphology 8 Pacing Site 13LVA, LRA, LA, RVOT, RVA, LVB, CSP, CSP, LLA, HRA, CSD, CSM, LVOTlu_abl_result 51 Intermediate pathway block - not reinducible, PartiallyIsolated, ORT Reinducible, Right bundle branch block, AV Node Block, AVNode Modified, Fast pathway block - not reinducible, VT Not-reinducible,Conduction Block, Isolated, AVNRT Reinducible, Mitral Isthmus Block(bidirectional), ORT Not Reinducible, Bidirectional CTI Block, AFLTerminated, PVCs eliminated, LLPV Isolated, Left bundle branch block, VTSlowed, WPW Terminated, FAT terminated, ORT Terminated, Reduction inelectrogram amplitude to less than 0.5 mV, RMPV Isolated, AP block, notreinducible, RUPV Isolated, AF Terminated, Complete AV Block, Slowpathway block - not reinducible, AF Converted to AFL, AFL NotReinducible, AP Block, Reduction in electrogram amplitude to less than0., VT Terminated, Mitral Isthmus Conduction Delay Only, LUPV Isolated,Single AV nodal echo only, ART Reinducible, AF Termination, AP Block,Not Reinducible, ART Not Reinducible, ART Terminated, WPW Reinducible,Mitral Isthmus Block (unidirectional), CTI conduction delay, IncompleteAV Block, Mitral Isthmus Conduction Delay, AP block (antegrade andretrograde), RLPV Isolated, AP block (antegrade only), UnidirectionalCTI Block Structural 8 Atrial Septal Defect, Patent Foramen Ovale,Common OS Left, Observations Atrial Scarring, LA Thrombus, Common OSRight, Pericardial Effusion Termination 11 Cardioversion, Ablation,Burst, Verapamil, Adenosine, Methods Spontaneous, Metropolol, Pvc,Procainamide, Ibutilide, Pac Access Type 21 Direct Cutdown,Percutaneous, Epicardial, Swan-Ganz Line, Tunneled Central Line,Arterial Line, Central Venous Pressure Line, Sheath - Hansen, Sheath -Transseptal, Peripherally Inserted Central Catheter, Pulmonary ArteryCatheter, Shunt, Sheath - Steerable, Sheath - Standard short, Sheath -Preformed long, Central Venous Line, Peripheral IV, Implantable Port

FIG. 4 shows an example screenshot 150 from the data-analytics module.The screenshot 150, for example, could be taken from a GUI of a website.It features three ‘areas’ of analytics that, informally, vary in termsof their complexity. On the left-hand side of the screenshot 150 aresimple pie charts that indicate basis statistics associated with ahospital performing EP procedures. The pie charts render graphicsindicating monthly statistics, and show information such as patientvolumes, manufacturers of IDs, patient demographics, and financialreimbursements. Similar statistics for previous months are renderedafter the user selects the desired month and updates the plots usingbuttons and pull-down menus at the bottom of the page. The middle areaof the page shows monthly bar charts that show similar data for theentire year. Each bar chart and its neighboring pie chart plots similardata. The right-hand area of the screenshot 150 shows a series ofpersistent buttons that allow clinicians to perform patient follow-up,generate pre-determined reports, and do standard and user-selectabledata analyses. These analytical processes are described in more detailbelow.

The screenshot 151 shown in FIG. 5 is rendered when the user selects‘Leads’ in the Patient Follow-Up section on the screenshot's right-handside, and then clicks the ‘Go’ button. Doing this allows the clinicianto enter a patient's name in a field on the screenshot's left-hand side,and then search for reports associated with the selected patient. Forexample, the clinician can search for PDF documents that describe avariety of patient-specific data, e.g. clinic visits, IDs associatedwith the patient, and advisory actions (e.g. recalls) associated withthe ID and its ancillary components (e.g. its leads). FIG. 6, forexample, shows a screenshot 152 indicating an advisory action for an IDlead (the Sprint Fidelis lead) manufactured by St. Jude Medical. Thedata-analytics module stores this information in the database, and afterrendering it on the screenshot 151, allows the clinician to email it andother information (e.g. reports and other documentation) to the patient,insurance company, or another clinician. In embodiments, thedata-analytics module includes software for character recognition thatallows content to be extracted from the PDF reports, and then usedafterwards in numerical calculations. As shown in FIG. 7, thedata-analytics module can render a screenshot 153 that shows multiplereports, all rendered on a single page. As described above, usingcharacter-recognition software, the data-analytics module can extractinformation from these reports and then use it for subsequentcalculations.

FIG. 8 shows a screenshot 154 that indicates how the data-analyticsmodule can perform standard, pre-determined analyses. As indicated inthe left-hand side of the screen shot, standard fields can be used toselect a given patient and then a data field corresponding to thepatient. In this case, the data field is an ECG waveform. As shown inthe middle section of the screen shot, once selected, the ECG waveformis plotted in a time-dependent form, and can be processed with a varietyof pre-determined algorithms, such as digital filters to remove selectedhigh and low-frequency components, a ‘beatpicker’ that detects astandard QRS complex associated with each heartbeat, and other customanalyses. As shown in the lower plot in the figure, in this example theECG waveform is sequentially processed with a digital bandpass filter, aderivative filter to remove any slowly varying components, and finallysquared to emphasize the QRS complex. The processed waveform is thenanalyzed with a beatpicker to find each QRS complex, as indicated by theround circle overlapped with each peak. Such custom analyses, forexample, can be programmed into the data-analytics module, and thenoperated when the user clicks the ‘Custom Analysis 1’ button. Ingeneral, any numerical algorithms or other computational techniques canbe programmed into the data-analytics module using this technique.Computer languages such as C and Java, or scripted language such asMatlab or PERL, can be used to program in the custom analyses. Oncethese or any other analyses are performed, the resulting data can beadded to a report, along with user-generated text that describes contentwithin the report. The data can also be exported for off-line numericalanalysis.

FIG. 9 shows a screenshot 155 that indicates an example of a customreport. Here, data from the EP ablation system (item 165 in FIG. 1;Table 1 shows data from this system) is accessed by the data-analyticssystem. As shown by the images in the middle of the screenshot 155, theGUI renders data from the EP ablation system as three-dimensional imagesthat show an approximation of the patient's heart and locations of wherethe ablations actually take place. Next to this image is a secondaryimage of the patient's heart that graphically indicates conductionpathways therein. Such a simulated image can be approximated from theECG waveform, as described in U.S. Pat. No. 7,751,875 to Bojovic et al,the contents of which are fully incorporated herein by reference. As anexample of a custom analysis, the ablation locations determined from theEP ablation system could be correlated with either the morphology of thepatient's ECG waveform or their associated cardiac rhythm, or with athree-dimensional image of the patient's heart simulated from the ECGwaveform. Such images may yield information such as damage to the heartor locations of blockages. Other similar correlations are within thescope of this invention.

The data-analytics module also facilitates processing of data generatedby the patient's ID and interrogated by a programmer. Alternatively,these data can be secured by accessing the Paceart system describedabove. Paceart organizes and archives data for IDs across differentmanufacturers (e.g. Medtronic, St. Jude, Boston Scientific, Biotronix),and serves as a central repository for a patient's arrhythmiainformation. It provides a gateway through which data flows to aclinic's EHR from programmers that collect data from IDs. As shown inthe screen shot 156 in FIG. 10, the data-analytics module allows a userto select a patient and the corresponding data field, generated by theID, for the patient. In the screenshot 156, the data field is thebradycardia programmed right ventricular refractory period. After theyare selected, these data are plotted in the screenshot, with the y-axisrepresenting the value of the refractory period, and the x-axisrepresenting consecutive heartbeats. Each data field generated by the IDand interrogated by the programmer, or alternatively stored in Paceart,can be analyzed in this manner. As described above, more complexanalyses are also possible. In general, the data-analytics module allowsdata fields to be analyzed across patients, devices, hospitals, genders,etc. In this way, for example, the efficacy and performance of the IDcan be evaluated using a large number of diverse subjects. This can bedone without the expense and time associated with a conventionalclinical trial.

FIG. 11 shows a screenshot 157 that indicates how the data-analyticsmodule can perform sophisticated, user-selectable analyses using datacollected as described above. As shown in the figure, the screenshot 157lists data fields associated with the hospital/clinic, patient,categories of physiological and device-related data, and individual datafields within the particular category. These data sets can be collectedfrom a large number of patients and hospitals, and then grouped togetherfor analysis. Once the data are selected, buttons shown on thescreenshot 157 can be used to add the data to a data table, such as onefound in a relational database or simple spreadsheet. A similar buttoncan then be used to export the data table, thus allowing the user toanalyze it using tools external to the data-analysis module, e.g.custom-written computer programs or tools such as Matlab. In this way,the data-analytics module provides sophisticated sets of data to theuser, thus allowing a wide range of analyses. In general, by organizingdata in this manner, the data-analytics module functions as a searchengine that has access to valuable clinical data normally not availablefor public consumption. These data are organized and categorized by thedata-collection/storage module to ensure their consistency and quality.In this manner, the system allows for sophisticated data analysisnormally reserved for expensive and time-consuming clinical trials.

The above-mentioned system can be used to generate clinical analyses andsubsequent reports for the clinician that include the followinginformation:

1—physiological information before and after EP treatment

2—ECG waveforms and their various components before and after treatment

3—estimated efficacy of EP treatment

4—the need for EP treatment

5—correlation of patient demographics and EP efficacy

6—correlation of physiological information and EP efficacy

7—correlation between ablation characteristics (e.g. ablationpotentials, locations) and stabilization of cardiac rhythm

8—efficacy of ID/leads and stabilization of cardiac rhythm

9—ID battery voltage and stabilization of cardiac rhythm

10—correlation between heart rate variability and occurrence of cardiactrauma (e.g. stroke, myocardial infarction) within well-defined periodsof time

Other clinical analyses are made possible with the invention describedhere, and are thus within its scope.

FIG. 12 shows an example of an ECG waveform 150 that is measured from apatient (e.g., before the EP procedure), stored in the database, andthen analyzed by an algorithmic-based tool such as that described withreference to FIG. 8 to estimate the patient's cardiac performance. TheECG waveform, which in this case corresponds to a relatively healthypatient, 150 features a collection of equally spaced, time-dependentdata points that are defined by a sampling rate of an ECG monitor (suchas that shown in FIG. 15), which in this case in 500 Hz. The waveformfeatures a sharply varying peak, called the QRS complex, which indicatesinitial depolarization of the heart and informally marks the onset ofthe patient's cardiac cycle. Each heartbeat yields a new QRS complex.After a few hundred milliseconds, a relatively slowly varying featurecalled the T-wave follows the QRS complex. In general, each patientfeatures a unique ECG waveform from which the algorithmic-based toolscan extract important cardiac information. As described above withreference to FIG. 8, a simple algorithmic-based tool called a‘beatpicker’ analyzes the ECG waveform 150 to determine the patient's HRand arrhythmia information. In this application, the beatpicker uses analgorithm (called the Pan-Thompkins algorithm) that determines thetemporal location of the QRS complex corresponding to each heartbeat.The Pan-Thompkins algorithm typically includes the following steps: i)filtering the ECG waveform to remove any high-frequency noise; ii)taking a mathematical derivative of the waveform; iii) squaring thewaveform; iv) signal averaging the waveform; and v) finding the peaks ofthe waveform processed with steps i)-iv). Locations of the QRS complexfrom waveforms processed in this manner are shown in the figure by acollection of gray squares 152. Once the collection of QRS complexes islocated, the algorithmic-based tool can determine the patient's HR andarrhythmia information using well-known techniques in the art.

The ECG waveform 150 described above is relatively simple, and otherthan a relatively tall T-wave, lacks any complicated features thatchallenge conventional beatpickers. However, such features are notuncommon amongst cardiac patients, and thus the beatpicker must besophisticated enough to analyze them. Moreover, the ECG waveform 172shown in FIG. 12 only corresponds to a single lead, and thus isrelatively unsophisticated and lacks information describing complexcardiovascular performance. Typically, the system according to thisinvention analyzes multi-lead ECG waveforms 180, such as those shown inFIG. 13. Multi-lead ECG waveforms can contain information from 5, 7, andeven 12-lead ECGs. In general, these types of ECG waveforms are requiredto evaluate the complex cardiovascular performance associated withpatients that would most benefit from the present invention.

For example, in embodiments, algorithmic-based tools according to theinvention, or software associated with these tools, can also analyzerelatively long traces of ECG waveforms (spanning over seconds orminutes) measured before, during, and after the EP procedure tocharacterize: i) a given patient; ii) the efficacy of the EP procedureapplied to that patient; iii) a given patient's need for an EPprocedure; or iv) the overall efficacy of the EP procedure as applied toa group of patients. Analysis of the relatively long traces of ECGwaveforms in this manner may indicate cardiac conditions such as cardiacbradyarrhythmias, blockage of an artery feeding the heart, acutecoronary syndrome, advanced age (fibrosis), inflammation (caused by,e.g., Lyme disease or Chaga's disease), congenital heart disease,ischaemia, genetic cardiac disorders, supraventricular tachycardia suchas sinus tachycardia, atrial tachycardia, atrial flutter, atrialfibrillation, junctional tachycardia, AV nodal reentry tachycardia andAV reentrant tachycardia, reentrant tachycardia, Wolff-Parkinson-White(WPW) Syndrome, Lown-Ganong-Levine (LGL) Syndrome, and ventriculartachycardia. Likewise, analysis of these cardiac conditions by analyzingthe ECG waveforms may indicate the efficacy of the EP procedure.

Typically, before the algorithmic-based tool deploys the beatpicker, itis analyzed against well-known databases, such as the MIT arrhythmiadatabase or the American Heart Association database, to determine itsperformance. Beatpickers with a performance of about 95% or greater, asevaluated relative to these standards, are typically categorized asacceptable. Alternatively, as described above, the algorithm-based toolsmay integrate with commercially available tools for analyzing ECGwaveforms, such as those developed and marketed by Mortara.

FIG. 14 shows a waveform snippet 182 found within the ECG waveform thatis shown in the dashed box 181 of FIG. 13. The waveform snippet 182corresponds to a single heartbeat. Waveform snippets 182 may becollected before, during, and after an EP procedure, and are typicallyanalyzed after they are stored in the database, as described above.Algorithm-based tools within the system, or software components withinthe algorithm-based tools, may analyze one or more waveform snippets 182generated by a given patient to predict certain cardiac conditionsassigned to that patient. Alternatively, the software may collectivelyanalyze waveform snippets corresponding to large groups of patients toevaluate, e.g., the efficacy of a certain aspect of an EP procedure, orpredict how a given EP procedure is likely to affect a given patient.

As shown in the figure, the waveform snippet features the followingcomponents: i) a QRS complex; ii) a P-wave; iii) a T-wave; iv) a U-wave;v) a PR interval; vi) a QRS interval; vii) a QT interval; viii) a PRsegment; and ix) an ST segment. Algorithmic-based tools within thesystem, or software associated with the algorithm-based tools, cananalyze each of these components and their evolution over time asdescribed above. In particular, algorithmic-based tools that performnumerical fitting or pattern recognition may be deployed to determinethe components and their temporal and amplitude characteristics for anygiven heartbeat recorded by the system. Each component corresponds to adifferent feature of the patient's cardiac system. For example, the PRinterval (which typically has a duration between about 120-200 ms)represents the time from firing of the patient's SA node to the end ofthe delay of their AV node. A prolonged PR interval, or a PR intervalthat is inconsistent over time, may indicate blockage of an arteryfeeding the patient's heart. Alternatively, a shortened or non-existentPR interval may indicate a cardiac condition such as tachycardic,junctional, ectopic, or ventricular rhythms. The QRS interval, which istypically between 40-100 ms, represents the travel time of electricalactivity through the patient's ventricles and ventricular depolarizationthat drives contraction of the heart. QRS intervals that are longer thanthis, or that feature a ‘notch’, can indicate aberrant ventricularactivity or cardiac rhythms with a ventricular focus.

Variation in the time between subsequent QRS complexes (i.e., the timeassociated with a given HR) may also indicate a cardiac condition. Ingeneral, some variation in this component is normal and indicative of ahealthy heart. Little or no variation, which typically becomes morepronounced as the patient ages, or a sudden decrease in variation, mayindicate the onset of a cardiac event.

The QT interval, which is typically less than 50% of the total durationof the time associated with the patient's HR, represents the travel timeof electrical activity through the patient's ventricles to the end ofventricular repolarization. This parameter varies with HR, and also withage and gender. Prolonged QT intervals represent a prolonged time tocardiac repolarization, and may indicate the onset of ventriculardysrhythmias.

The P-wave, which proceeds the QRS complex of each heartbeat, istypically upright and uniform in shape, and indicates the firing of theSA node and subsequent atrial depolarization; it typically has a widthof about 50 ms, and an amplitude that is about 10-20% of the QRSamplitude. P waves that are abnormally wide or notched, or tall andpeaked, indicate cardiac conditions such as P-mitrale and P-pulmonale,respectively. The PR segment, which separates this feature from the QRScomplex, is typically 120-200 ms in duration, and represents the delayseparating the firing of the SA node and ventricular depolarization. APR segment that gradually increases over time may indicate the onset ofdamage to the patient's heart. The T-wave, which follows the QRScomplex, indicates the onset of ventricular repolarization, and shouldappear rounded and somewhat symmetrical; the peak of the T-wave istypically relatively close to the wave's end. T-waves that areabnormally tall or ‘tented’ may indicate cardiac conditions such ashyperkalemia or myorcardial injury. T-waves that are inverted mayindicate cardiac conditions such as myocardial ischemia, myocardialinfarction, pericarditis, ventricular enlargement, bundle branch block,subarachnoid hemorrhage, and the presence of certain pharmaceuticalcompounds, such as quinidine or procainamide.

The U-wave, which is somewhat uncommon and when present only about 2-5%of the amplitude of the QRS complex, depicts the last phase ofventricular repolarization. It is typically present with patientsundergoing bradycardia, and can be enlarged during cardiac conditionssuch as hypokalemia, cardiomyopathy, or enlargement of the leftventricle.

FIG. 15 shows an example of a body-worn ECG monitoring system 199according to the invention that continuously monitors ECG waveforms suchas those shown in FIG. 12 from an ambulatory patient 198. The body-wornECG monitoring system 199 features a control unit 200 that featuresanalog electronics for measuring analog ECG waveforms, a processing unitfor digitizing the analog ECG waveforms and then processing them asdescribed above to determine HR and arrhythmia information, and awireless transmitter for sending this information to a remote wirelessmonitor 230. During use, the control unit 200 connects to a cable 210that, in turn, connects to a collection of ECG leads 220 a-c. FIG. 15shows a 3-lead system, but it is understood that the present inventioncould also include 5, 7, and 12-lead ECG systems. Each ECG lead 220 a-cterminates with an ECG electrode 208 a-c that adheres to the patient'sskin and typically connects to the associated lead with a standard snapconnector (not shown in the figure). In this case, the 3 ECG electrodes208 a-c are deployed on the patient's chest in a standard ‘Einthoven'sTriangle’ configuration, meaning individual electrodes are attached tothe upper left-hand (electrode 208 a), upper right-hand (electrode 208b), and lower right-hand (electrode 208 c) portions of the patient'storso. During use, the electrodes measure weak analog electrical signalsfrom these locations, and transmit these through their respective leads220 a-c to the processing unit 200, which then processes the signalswith the analog circuit to determine one or more analog ECG waveforms.An analog-to-digital converter then digitizes these and avails them to amicroprocessor, which runs computer code corresponding to the beatpickerthat picks out the appropriate features (e.g. the QRS complexcorresponding to each heartbeat) and then analyzes them as describedabove. The processing unit then wirelessly transmits this and otherinformation (e.g. digitized ECG waveforms) to the remote monitor 230.This system typically includes a computer server that connects through awired connection to an Internet-based system 232, which in turnintegrates with the system according to the invention, as shownschematically in FIG. 1. With this configuration, ECG waveforms measuredbefore, during, and after the EP procedure can be collected and furtheranalyzed by additional algorithm-based tools, such as those describedabove, to evaluate the patient's cardiac performance.

Other embodiments to the ECG monitoring system 199 shown in FIG. 15, ofcourse, are within the scope of the invention. For example, the system199 can include additional physiological sensors, such as those thatmeasure other vitals such as BP, RR, SpO2, and body temperature. Thesensors can also measure physiological parameters that are not vitalsigns, such as stroke volume and cardiac output. In general, anyphysiological parameter (either numerical value or time-dependentwaveform) can be measured with systems similar to those described above,and then stored in the database shown in FIG. 1 and analyzed withalgorithm-based tools to characterize the patient.

Additionally, the body-worn monitor 199 described with reference to FIG.15 can take many different forms. For example, the monitor 199 canconnect to the remote monitor through a wired connection as opposed to awireless one. The monitor 199 may also deploy body-worn sensors (e.g.the electrodes 208 a-c) in configurations that are different than thosedescribed above. The body-worn monitor 199 may also connect directly tothe Internet-based system 232, thus bypassing the remote monitor.Different systems with different configurations may also be used tomonitor the patient before, during, and after the EP procedure. All ofthese configurations are within the scope of the invention.

In other embodiments, a separate hardware system can be used to collectdata from various programmers, and then forward the data into thedata-analytics module described above using either wired or wirelesstechnologies. This hardware system can take a variety of forms, andindividual hardware systems may be used for each piece of equipment thatsupplies data to the data collection-storage module. For example, such ahardware system utilizing a wireless system may be particularly usefulin hospital environment where it is simply not practical to connectsystems through a wired connection. Referring to FIG. 16, such ahardware system, referred to herein as a ‘Perminova Box’ 300, featuresinterfaces for Ethernet 301, Bluetooth/WiFi 301, or cellular 304performs this function. During use, the Perminova Box 300 integrateswith hardware systems such as device programmers 305 or EP ablationsystems 320 through one of the above-mentioned hardware interfaces. Itmay feature a form factor of a single-board computer, tablet computer,or programmable USB ‘dongle’ that plugs into an available port on thedevice programmer 305 and/or the EP ablation system. In embodiments, theprogrammable USB dongle operates computer code that recognizes when anew file is received, parses the new file to strip out the appropriatedata fields, and then transmits these over a wireless connection to thedata-collection/storage module. In this way, the Perminova Box 300 canreceive data from these devices (typically in the form of XML or PDFfiles), and in response supply data to the data collection/storagemodule 310, and eventually to the data-analytics module 315 forprocessing.

Other embodiments are also within the scope of the invention. Forexample, other techniques besides the above-described algorithms can beused to analyze data collected with the system. Additionally, processingunits and probes for measuring ECG waveforms similar to those describedabove can be modified and worn on other portions of the patient's body.For example, the ECG-measuring system can be in a patch configuration.Or they can be modified to attach to other sites that yield ECGwaveforms, such as the back or arm. In these embodiments the processingunit can be worn in places other than the wrist, such as around the neck(and supported, e.g., by a lanyard) or on the patient's waist(supported, e.g., by a clip that attaches to the patient's belt). Instill other embodiments the probe and processing unit are integratedinto a single unit. In still other embodiments, the systems formeasuring ECG waveforms are implanted or inserted in the patient, e.g.they are part of the ID or EP system.

Systems similar to that described above can also be used for othercardiac procedures conducted in other areas of the hospital, such as thecatheterization laboratory, medical clinic, or vascular analysislaboratory. In these applications, data other than HR and ECG waveformsmay be analyzed using techniques similar to those described above. Dataused in these examples includes medical images (such as those measuredusing MRI or Doppler/ultrasound), all vital signs, hemodynamicproperties such as cardiac output and stroke volume, tissue perfusion,pH, hematocrit, and parameters determined with laboratory studies.

Still other embodiments are within the scope of the following claims.

What is claimed is:
 1. A system for determining the presence of cardiacabnormalities in a patient by evaluating T waves from the patient's ECGwaveform, comprising: (i) a body-worn ECG monitoring system configuredto obtain ECG waveforms from the patient comprising: at least 2 ECGelectrodes, a processing unit comprising a first microprocessor, ananalog-to-digital converter, and a wireless transmitter, the processingunit operably connected to the ECG electrodes and configured to receiveand process electrical signals therefrom to generate one or more digitalECG waveforms, the processing unit further configured to identify one ormore features corresponding to one or more heartbeats in the one or moredigital ECG waveforms and to wirelessly transmit the one or morefeatures and the digital ECG waveforms; (ii) a computer systemcomprising: a database stored on a non-transitory computer-readablestorage medium, the database comprising a set of data fields collectedfrom a plurality of individuals, with each data field in the setcorresponding to an individual within the plurality of individuals andincluding a T wave extracted from an ECG waveform obtained from theindividual and a parameter indicating the presence of a cardiacabnormality in the individual, and a second microprocessor operablyconnected to the processing unit to receive the one or more features andthe one or more digital ECG waveforms wirelessly transmitted by theprocessing unit, and operably connected to the non-transitorycomputer-readable storage medium to access the database to store the oneor more features and the one or more digital ECG waveforms in a datafield corresponding to the patient; wherein the second microprocessorperforms an ECG-analysis algorithm that processes the one or more ECGwaveforms to extract therefrom a set of parameters corresponding to theT wave in the one or more heartbeats, wherein the second microprocessorperforms an averaging algorithm by processing a plurality of datafields, each data field corresponding to an individual within theplurality of individuals to determine an average correlation factormapping the parameters corresponding to the T wave to the parameterindicating the presence of a cardiac abnormality, and wherein the secondmicroprocessor performs a correlation algorithm configured to evaluatethe presence of a cardiac abnormality in the patient by comparing theset of parameters corresponding to the T wave in the one or moreheartbeats to the average correlation factor to estimate a new parameterindicating the presence of a cardiac abnormality in the patient, and tostore the new parameter in the data field corresponding to the patient.2. The system of claim 1, wherein the ECG-analysis algorithm is furtherconfigured to process the ECG waveform to determine the temporallocation of the T wave.
 3. The system of claim 1, wherein theECG-analysis algorithm is further configured to process the ECG waveformto determine the amplitude of the T wave.
 4. The system of claim 3,wherein the average correlation factor correlates an amplitude of the Twave to the presence of a cardiac abnormality in the patient.
 5. Thesystem of claim 4, wherein the correlation algorithm is furtherconfigured to determine the presence of a cardiac abnormality in thepatient when the amplitude of the T wave is less than a baseline of theECG waveform.
 6. The system of claim 5, wherein the correlationalgorithm is further configured to determine the presence of at leastone of myocardial ischemia, myocardial infarction, pericarditis,ventricular enlargement, bundle branch block, subarachnoid hemorrhagewhen the amplitude of the T wave is less than a baseline of the ECGwaveform.
 7. The system of claim 5, wherein the correlation algorithm isfurther configured to determine the presence of a cardiac abnormality inthe patient when the amplitude of the T wave is inverted.
 8. The systemof claim 5, wherein the correlation algorithm is further configured todetermine the presence of at least one of myocardial ischemia,myocardial infarction, pericarditis, ventricular enlargement, bundlebranch block, and subarachnoid hemorrhage when the amplitude of the Twave is inverted.
 9. The system of claim 1, wherein the database,ECG-analysis algorithm, averaging algorithm, and correlation algorithmare comprised by an Internet-accessible system.
 10. The system of claim9, wherein the Internet-accessible system further comprises a graphicaluser interface (GUI).
 11. The system of claim 10, wherein the GUIcomprises a webpage that displays the new parameter indicating thepresence of at least one of myocardial ischemia, myocardial infarction,pericarditis, ventricular enlargement, bundle branch block, andsubarachnoid hemorrhage.
 12. The system of claim 1, wherein theECG-measuring system comprises an ECG circuit configured to be worn onthe patient's body, and a transmitting circuit configured to transmit anumerical representation of the ECG waveform to the database.
 13. Thesystem of claim 12, wherein the transmitting circuit is configured totransmit the numerical representation of the ECG waveform to an Ethernetport, which then transmits the numerical representation of the ECGwaveform to an IP address associated with the database.
 14. The systemof claim 12, wherein the transmitting circuit is configured to transmitthe numerical representation of the ECG waveform to a mobile device,which then transmits the numerical representation of the ECG waveform toan IP address associated with the database.
 15. A system for evaluatinga presence of at least one of myocardial ischemia, myocardialinfarction, pericarditis, ventricular enlargement, bundle branch block,and subarachnoid hemorrhage in a patient, comprising: (i) a body-wornECG monitoring system comprising: at least 3 ECG electrodes, aprocessing unit comprising a first microprocessor, an analog-to-digitalconverter, and a wireless transmitter, the processing unit operablyconnected to the ECG electrodes and configured to receive and processelectrical signals therefrom to generate one or more digital ECGwaveforms, the processing unit further configured to identify one ormore features corresponding to each heartbeat in the one or more digitalECG waveforms and to wirelessly transmit the one or more features andthe digital ECG waveforms; (ii) a computer system comprising: a databasestored on a non-transitory computer-readable storage medium, thedatabase comprising a set of data fields collected from a plurality ofindividuals, with each data field in the set corresponding to anindividual within the plurality of individuals and including a T waveextracted from an ECG waveform obtained from the individual and aparameter indicating the presence of at least one of myocardialischemia, myocardial infarction, pericarditis, ventricular enlargement,bundle branch block, and subarachnoid hemorrhage in the individual, anda second microprocessor operably connected to the processing unit toreceive the one or more features and the one or more digital ECGwaveforms wirelessly transmitted by the processing unit, and operablyconnected to the non-transitory computer-readable storage medium toaccess the database to store the one or more features and the one ormore digital ECG waveforms in a data field corresponding to the patient;wherein the second microprocessor performs an ECG-analysis algorithmthat processes the one or more ECG waveforms to extract therefrom a setof parameters corresponding to the T wave in the one or more heartbeats,wherein the second microprocessor performs an averaging algorithm byprocessing a plurality of data fields, each data field corresponding toan individual within the plurality of individuals to determine anaverage correlation factor mapping the set parameters corresponding tothe T wave to the parameter indicating at least one of myocardialischemia, myocardial infarction, pericarditis, ventricular enlargement,bundle branch block, and subarachnoid hemorrhage, and wherein the secondmicroprocessor performs a correlation algorithm configured to evaluatethe presence of at least one of myocardial ischemia, myocardialinfarction, pericarditis, ventricular enlargement, bundle branch block,and subarachnoid hemorrhage in the patient by comparing the set ofparameters corresponding to the T wave in the one or more heartbeats tothe average correlation factor to estimate a new parameter indicatingthe presence of at least one of myocardial ischemia, myocardialinfarction, pericarditis, ventricular enlargement, bundle branch block,and subarachnoid hemorrhage, and to store the new parameter in the datafield corresponding to the patient.